Using Crowding-Distance in a Multiobjective Genetic Algorithm for Protein Structure Prediction

In this paper the insertion of the crowding-distance technique in a multiobjective genetic algorithm with phenotypic crowding is carried out for the protein structure prediction (PSP) problem. The main goal is obtain a more diversified and well distributed Pareto frontiers at the end of the optimization process. Three classical force field potentials, three hydrogen bond potentials and a hydrophobic compactation term were combined in two con figurations with different objectives for the fitness function. A set of 45 proteins was used to evaluate the performance of the predictions. The results were compared against the previous mono-and multiobjective approaches, and with QUARK and MEAMT, two consolidated free-modeling PSP methodologies. The strategy proposed here was able to obtain improvements in the predicted models relative to the previous mono-and multiobjective approaches, proving to be quite promising in dealing with the PSP problem.